{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "source": [
    "# Summarize Data\n",
    "\n",
    "The `features` module packs a set of data summarization tools to calculate total counts, lengths, areas, and basic descriptive statistics of features and their attributes within areas or near other features. You can access these tools using the `summarize_data` sub module."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Summarize-Data\" data-toc-modified-id=\"Summarize-Data-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Summarize Data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Aggregate-points\" data-toc-modified-id=\"Aggregate-points-1.1\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Aggregate points</a></span><ul class=\"toc-item\"><li><span><a href=\"#Aggregate-earthquakes-by-state\" data-toc-modified-id=\"Aggregate-earthquakes-by-state-1.1.1\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>Aggregate earthquakes by state</a></span></li></ul></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregate points\n",
    "In this example, let us observe how to use `aggregate_points` tool to summarize data from spot measurements by area. To learn more about this tool and the formula it uses, refer to the documentation [here](http://doc.arcgis.com/en/arcgis-online/use-maps/aggregate-points.htm)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# connect to GIS\n",
    "from arcgis.gis import GIS\n",
    "gis = GIS(\"portal url\", \"username\", \"password\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=12661724521644babf4be1b65948be06' target='_blank'>\n",
       "                        <img src='' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=12661724521644babf4be1b65948be06' target='_blank'><b>World Earthquake Locations</b>\n",
       "                        </a>\n",
       "                        <br/>Data from USGS global earthquake archive.<img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by api_data_owner\n",
       "                        <br/>Last Modified: July 26, 2023\n",
       "                        <br/>0 comments, 23 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"World Earthquake Locations\" type:Feature Layer Collection owner:api_data_owner>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq_search = gis.content.search(query=\"title:World Earthquake Locations owner:api_data_owner\", item_type=\"Feature Layer Collection\", max_items=1)\n",
    "eq_item = eq_search[0]\n",
    "eq_item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=e6bb437ebd50445697132b845e13fb09' target='_blank'>\n",
       "                        <img src='' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=e6bb437ebd50445697132b845e13fb09' target='_blank'><b>USA States (Generalized)</b>\n",
       "                        </a>\n",
       "                        <br/>USA States (Generalized) provides 2017 boundaries for the States of the United States in the 50 states and the District of Columbia.<img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/featureshosted16.png' style=\"vertical-align:middle;\" width=16 height=16>Feature Layer Collection by arcgis_python\n",
       "                        <br/>Last Modified: April 20, 2021\n",
       "                        <br/>0 comments, 49 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"USA States (Generalized)\" type:Feature Layer Collection owner:arcgis_python>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# search for USA states - area / polygon data\n",
    "states_search = gis.content.search(\"title:'USA States (Generalized)'\", \n",
    "                                   \"feature layer\", max_items=-1)\n",
    "states_item = states_search[0]\n",
    "states_item"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's draw the layers on a map and observe how they are distributed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=></img>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "map1 = gis.map(\"USA\")\n",
    "map1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "map1.center = [36, -98]\n",
    "map1.zoom = 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "map1.content.add(states_item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "map1.content.add(eq_item)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate earthquakes by state\n",
    "As you can see, a number of earthquakes fall on the boundary of tectonic plates ([ring of fire](https://en.wikipedia.org/wiki/Ring_of_Fire)). However, there are a few more dispersed into other states. It would be interesting to aggregate all the earthquakes by state and plot that as a chart. \n",
    "\n",
    "The `aggregate_points` tool in the `summarize_data` sub module is a valid candidate for such analyses. The example below shows how to run this tool using ArcGIS API for Python.\n",
    "\n",
    "To start with, let us access the layers in the states and earthquakes items and view their attribute information to understand how the data can be summarized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq_fl = eq_item.layers[0]\n",
    "states_fl = states_item.layers[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have accessed the layers in the items as `FeatureLayer` objects. We can query the `fields` property to understand what kind of attribute data is stored in the layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event_Id\n",
      "Community_Decimal_Intensity\n",
      "Event_Detail\n",
      "Distance_from_Epicenter__degree\n",
      "Felt_Reports\n",
      "Azimuthal_Gap__degrees_\n",
      "Event_Magnitude\n",
      "Magnitude_Type\n",
      "Shake_Intensity\n",
      "Source_Network\n",
      "Seismic_Stations\n",
      "Location_Name\n",
      "Residual_Travel_Time__seconds_\n",
      "Event_Significance\n",
      "Network_Sources\n",
      "Review_Status\n",
      "Event_Time\n",
      "Event_Title\n",
      "Tsunami_Warning\n",
      "Event_Type\n",
      "Product_Types\n",
      "Last_Updated\n",
      "Event_Page\n",
      "Latitude\n",
      "Longitude\n",
      "Elevation__meters_\n",
      "Depth__kilometers_\n",
      "OBJECTID\n",
      "Event_Time_1\n",
      "Event_Type_1\n",
      "ObjectId2\n"
     ]
    }
   ],
   "source": [
    "#query the fields in eq_fl layer\n",
    "for field in eq_fl.properties.fields:\n",
    "    print(field['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "field_names = [f[\"name\"] for f in states_fl.properties.fields]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "FID              STATE_NAME       STATE_FIPS       SUB_REGION       STATE_ABBR       \n",
      "BLACK            AMERI_ES         ASIAN            HAWN_PI          HISPANIC         \n",
      "AGE_5_9          AGE_10_14        AGE_15_19        AGE_20_24        AGE_25_34        \n",
      "AGE_85_UP        MED_AGE          MED_AGE_M        MED_AGE_F        HOUSEHOLDS       \n",
      "MHH_CHILD        FHH_CHILD        FAMILIES         AVE_FAM_SZ       HSE_UNITS        \n",
      "CROP_ACR12       AVE_SALE12       SQMI             Shape__Area      Shape__Length    \n",
      "Shape__Area_2    Shape__Length_2  \n"
     ]
    }
   ],
   "source": [
    "for a,b,c,d,e in zip(field_names[0::10], field_names[1::10], field_names[2::10], field_names[3::10], field_names[4::10]):\n",
    "    print(f\"{a:17}{b:17}{c:17}{d:17}{e:17}\")\n",
    "print(f\"{field_names[56]:17}{field_names[57]:17}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us aggreate the points by state and summarize the `magnitude` field and use `mean` as the summary type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "{\"cost\": 478.947}\n"
     ]
    }
   ],
   "source": [
    "# this cell will take a few minutes to execute because of several data points\n",
    "from arcgis.features import summarize_data\n",
    "sum_fields = ['Event_Magnitude Mean', 'Depth__kilometers_ Min']\n",
    "eq_summary = summarize_data.aggregate_points(point_layer = eq_fl,\n",
    "                                            polygon_layer = states_fl,\n",
    "                                            keep_boundaries_with_no_points=False,\n",
    "                                            summary_fields=sum_fields)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When running the tool above, we did not specify a name for the `output_name` parameter. Consequently, the analyses results were not stored on the portal, and instead stored in the variable `eq_summary`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aggregated_layer': <FeatureCollection>, 'group_summary': ''}"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "# access the aggregation feature colleciton\n",
    "eq_aggregate_fc = eq_summary['aggregated_layer']\n",
    "\n",
    "#query this feature collection to get a data as a feature set\n",
    "eq_aggregate_fset = eq_aggregate_fc.query()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`FeatureSet` objects support visualizing attribute information as a pandas dataframe. This is a neat feature since you do not have to iterate through each feature to view their attribute information.\n",
    "\n",
    "Let us view the summary results as a pandas dataframe table. Note, the `aggregate_points` tool appends the polygon layer's original set of fields to the analysis result in order to provide context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>STATE_NAME</th>\n",
       "      <th>STATE_FIPS</th>\n",
       "      <th>SUB_REGION</th>\n",
       "      <th>STATE_ABBR</th>\n",
       "      <th>POPULATION</th>\n",
       "      <th>POP_SQMI</th>\n",
       "      <th>POP2010</th>\n",
       "      <th>POP10_SQMI</th>\n",
       "      <th>WHITE</th>\n",
       "      <th>...</th>\n",
       "      <th>GlobalID</th>\n",
       "      <th>Shape__Area_2</th>\n",
       "      <th>Shape__Length_2</th>\n",
       "      <th>Shape_Length</th>\n",
       "      <th>Shape_Area</th>\n",
       "      <th>mean_Event_Magnitude</th>\n",
       "      <th>min_Depth__kilometers_</th>\n",
       "      <th>Point_Count</th>\n",
       "      <th>AnalysisArea</th>\n",
       "      <th>SHAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>56</td>\n",
       "      <td>Mountain</td>\n",
       "      <td>WY</td>\n",
       "      <td>598332</td>\n",
       "      <td>6.1</td>\n",
       "      <td>563626</td>\n",
       "      <td>5.8</td>\n",
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       "      <td>...</td>\n",
       "      <td>{9DC7C7AC-FE50-46FF-A98A-BC9B99E0CED0}</td>\n",
       "      <td>474117105945.773438</td>\n",
       "      <td>2775149.430913</td>\n",
       "      <td>2775149.430947</td>\n",
       "      <td>474117105972.575684</td>\n",
       "      <td>4.550545</td>\n",
       "      <td>-1.4</td>\n",
       "      <td>110</td>\n",
       "      <td>253293.67329</td>\n",
       "      <td>{\"rings\": [[[-11583195.4614, 5115880.653200000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>02</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>AK</td>\n",
       "      <td>744733</td>\n",
       "      <td>1.3</td>\n",
       "      <td>710231</td>\n",
       "      <td>1.2</td>\n",
       "      <td>473576</td>\n",
       "      <td>...</td>\n",
       "      <td>{FC1D546E-050D-40E0-8674-D380F839AD76}</td>\n",
       "      <td>8101401247192.390625</td>\n",
       "      <td>59249543.508505</td>\n",
       "      <td>59249543.507812</td>\n",
       "      <td>8101401247161.800781</td>\n",
       "      <td>4.474101</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>2802</td>\n",
       "      <td>1493247.177689</td>\n",
       "      <td>{\"rings\": [[[-17959594.8053, 8122953.575199999...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>38</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>ND</td>\n",
       "      <td>793399</td>\n",
       "      <td>11.2</td>\n",
       "      <td>672591</td>\n",
       "      <td>9.5</td>\n",
       "      <td>605449</td>\n",
       "      <td>...</td>\n",
       "      <td>{4C973C13-C535-45DD-B5CB-0BD837DAE90D}</td>\n",
       "      <td>400989888326.039062</td>\n",
       "      <td>2688189.824234</td>\n",
       "      <td>2688189.824286</td>\n",
       "      <td>400989888305.082947</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>183403.48984</td>\n",
       "      <td>{\"rings\": [[[-10990621.962, 5770462.616400003]...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>46</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>SD</td>\n",
       "      <td>880051</td>\n",
       "      <td>11.4</td>\n",
       "      <td>814180</td>\n",
       "      <td>10.6</td>\n",
       "      <td>699392</td>\n",
       "      <td>...</td>\n",
       "      <td>{2064228A-2CC4-4E74-B492-34EF98939518}</td>\n",
       "      <td>392302335744.371094</td>\n",
       "      <td>2824245.235541</td>\n",
       "      <td>2824245.23589</td>\n",
       "      <td>392302335746.93811</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>199912.09036</td>\n",
       "      <td>{\"rings\": [[[-11442350.6328, 5311257.036700003...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>10</td>\n",
       "      <td>South Atlantic</td>\n",
       "      <td>DE</td>\n",
       "      <td>971403</td>\n",
       "      <td>491.7</td>\n",
       "      <td>897934</td>\n",
       "      <td>454.5</td>\n",
       "      <td>618617</td>\n",
       "      <td>...</td>\n",
       "      <td>{C83CE019-1814-4B07-8111-6B414909808F}</td>\n",
       "      <td>8823228994.324219</td>\n",
       "      <td>535395.672895</td>\n",
       "      <td>535395.672768</td>\n",
       "      <td>8823228992.748075</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.87</td>\n",
       "      <td>1</td>\n",
       "      <td>5321.669252</td>\n",
       "      <td>{\"rings\": [[[-8427672.8732, 4658497.009999998]...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID    STATE_NAME STATE_FIPS          SUB_REGION STATE_ABBR  \\\n",
       "0         1       Wyoming         56            Mountain         WY   \n",
       "1         3        Alaska         02             Pacific         AK   \n",
       "2         5  North Dakota         38  West North Central         ND   \n",
       "3         6  South Dakota         46  West North Central         SD   \n",
       "4         7      Delaware         10      South Atlantic         DE   \n",
       "\n",
       "   POPULATION  POP_SQMI  POP2010  POP10_SQMI   WHITE  ...  \\\n",
       "0      598332       6.1   563626         5.8  511279  ...   \n",
       "1      744733       1.3   710231         1.2  473576  ...   \n",
       "2      793399      11.2   672591         9.5  605449  ...   \n",
       "3      880051      11.4   814180        10.6  699392  ...   \n",
       "4      971403     491.7   897934       454.5  618617  ...   \n",
       "\n",
       "                                 GlobalID         Shape__Area_2  \\\n",
       "0  {9DC7C7AC-FE50-46FF-A98A-BC9B99E0CED0}   474117105945.773438   \n",
       "1  {FC1D546E-050D-40E0-8674-D380F839AD76}  8101401247192.390625   \n",
       "2  {4C973C13-C535-45DD-B5CB-0BD837DAE90D}   400989888326.039062   \n",
       "3  {2064228A-2CC4-4E74-B492-34EF98939518}   392302335744.371094   \n",
       "4  {C83CE019-1814-4B07-8111-6B414909808F}     8823228994.324219   \n",
       "\n",
       "   Shape__Length_2     Shape_Length            Shape_Area  \\\n",
       "0   2775149.430913   2775149.430947   474117105972.575684   \n",
       "1  59249543.508505  59249543.507812  8101401247161.800781   \n",
       "2   2688189.824234   2688189.824286   400989888305.082947   \n",
       "3   2824245.235541    2824245.23589    392302335746.93811   \n",
       "4    535395.672895    535395.672768     8823228992.748075   \n",
       "\n",
       "   mean_Event_Magnitude  min_Depth__kilometers_  Point_Count    AnalysisArea  \\\n",
       "0              4.550545                    -1.4          110    253293.67329   \n",
       "1              4.474101                    -3.0         2802  1493247.177689   \n",
       "2                   5.5                     0.0            1    183403.48984   \n",
       "3                   4.3                     0.0            8    199912.09036   \n",
       "4                   4.1                    9.87            1     5321.669252   \n",
       "\n",
       "                                               SHAPE  \n",
       "0  {\"rings\": [[[-11583195.4614, 5115880.653200000...  \n",
       "1  {\"rings\": [[[-17959594.8053, 8122953.575199999...  \n",
       "2  {\"rings\": [[[-10990621.962, 5770462.616400003]...  \n",
       "3  {\"rings\": [[[-11442350.6328, 5311257.036700003...  \n",
       "4  {\"rings\": [[[-8427672.8732, 4658497.009999998]...  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation_df = eq_aggregate_fset.sdf\n",
    "aggregation_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's narrow down the output fields to view the output fields from the `summarize_data()` operation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATE_NAME</th>\n",
       "      <th>SUB_REGION</th>\n",
       "      <th>STATE_ABBR</th>\n",
       "      <th>POP2010</th>\n",
       "      <th>POP10_SQMI</th>\n",
       "      <th>mean_Event_Magnitude</th>\n",
       "      <th>min_Depth__kilometers_</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Mountain</td>\n",
       "      <td>WY</td>\n",
       "      <td>563626</td>\n",
       "      <td>5.8</td>\n",
       "      <td>4.550545</td>\n",
       "      <td>-1.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Pacific</td>\n",
       "      <td>AK</td>\n",
       "      <td>710231</td>\n",
       "      <td>1.2</td>\n",
       "      <td>4.474101</td>\n",
       "      <td>-3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North Dakota</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>ND</td>\n",
       "      <td>672591</td>\n",
       "      <td>9.5</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>West North Central</td>\n",
       "      <td>SD</td>\n",
       "      <td>814180</td>\n",
       "      <td>10.6</td>\n",
       "      <td>4.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>South Atlantic</td>\n",
       "      <td>DE</td>\n",
       "      <td>897934</td>\n",
       "      <td>454.5</td>\n",
       "      <td>4.1</td>\n",
       "      <td>9.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     STATE_NAME          SUB_REGION STATE_ABBR  POP2010  POP10_SQMI  \\\n",
       "0       Wyoming            Mountain         WY   563626         5.8   \n",
       "1        Alaska             Pacific         AK   710231         1.2   \n",
       "2  North Dakota  West North Central         ND   672591         9.5   \n",
       "3  South Dakota  West North Central         SD   814180        10.6   \n",
       "4      Delaware      South Atlantic         DE   897934       454.5   \n",
       "\n",
       "   mean_Event_Magnitude  min_Depth__kilometers_  \n",
       "0              4.550545                    -1.4  \n",
       "1              4.474101                    -3.0  \n",
       "2                   5.5                     0.0  \n",
       "3                   4.3                     0.0  \n",
       "4                   4.1                    9.87  "
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregation_df[[\"STATE_NAME\",\"SUB_REGION\",\"STATE_ABBR\",\"POP2010\",\"POP10_SQMI\",\"mean_Event_Magnitude\",\"min_Depth__kilometers_\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's narrow this data down further to only those states that have had more than 25 earthquakes over the span of this data (1900 - 2023)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "agg_gt25_df = aggregation_df.loc[aggregation_df[\"Point_Count\"] > 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(agg_gt25_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, over the time span of this data, only 15 of the 50 US States have had more than 25 earthquakes. Let us plot a bar chart to view which states had the most earthquakes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='STATE_NAME'>"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wcDBOnjyJqKgoJCQkoHHjxgCAJUuWwNfXF6dPn4anp+cLvUgiIiJ6/fynOTE5OTkAAFtbW43jMTExcHBwgIeHB/r164fMzEzpvkOHDuHhw4cICgqSjrm4uMDb2xvx8fEAgH379kGtVksBBgCaNGkCtVottXlSfn4+bt++rXEjIiKi19dLhxghBIYPH4533nkH3t7e0vE2bdpg9erV2LVrF2bPno2DBw+iRYsWyM/PBwBkZGTAyMgINjY2Gs/n6OiIjIwMqY2Dg0OJPh0cHKQ2T5o6dao0f0atVsPV1fVlXxoREREpwAudTipu8ODB+PfffxEXF6dxvEuXLtJ/e3t7o0GDBqhatSr+/PNPdOrUqcznE0JApVJJPxf/77LaFDd27FgMHz5c+vn27dsMMkRERK+xlxqJGTJkCLZs2YLdu3fjjTfeeGpbZ2dnVK1aFWfPngUAODk54cGDB8jOztZol5mZCUdHR6nNtWvXSjxXVlaW1OZJxsbGsLKy0rgRERHR6+uFQowQAoMHD8aGDRuwa9cuVKtW7ZmPuXHjBi5dugRnZ2cAQP369WFoaIjo6GipTXp6OpKTk9G0aVMAgK+vL3JycnDgwAGpzf79+5GTkyO1ISIioorthU4nff7551izZg02b94MS0tLaX6KWq2Gqakp7t69iwkTJuCDDz6As7MzUlNT8fXXX8Pe3h4dO3aU2vbt2xcjRoyAnZ0dbG1tMXLkSPj4+EhXK9WqVQshISHo168fFi9eDAD47LPP0K5dO16ZRERERABeMMT89NNPAICAgACN48uXL0evXr2gr6+PY8eOYcWKFbh16xacnZ0RGBiIdevWwdLSUmo/d+5cGBgYoHPnzsjLy0PLli0RHh4OfX19qc3q1asxdOhQ6Sqm9u3bIyws7GVfJxEREb1mXijECCGeer+pqSm2b9/+zOcxMTHBggULsGDBgjLb2NraYtWqVS9SHhEREVUg3DuJiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgUiSGGiIiIFIkhhoiIiBSJIYaIiIgU6YVCzNSpU9GwYUNYWlrCwcEB77//Pk6fPq3RRgiBCRMmwMXFBaampggICMDx48c12uTn52PIkCGwt7eHubk52rdvj8uXL2u0yc7ORmhoKNRqNdRqNUJDQ3Hr1q2Xe5VERET02nmhEBMbG4vPP/8cCQkJiI6OxqNHjxAUFITc3FypzYwZMzBnzhyEhYXh4MGDcHJyQuvWrXHnzh2pzbBhw7Bx40ZERkYiLi4Od+/eRbt27VBQUCC16datG5KSkhAVFYWoqCgkJSUhNDT0FbxkIiIieh2ohBDiZR+clZUFBwcHxMbGws/PD0IIuLi4YNiwYfjqq68APB51cXR0xPTp09G/f3/k5OSgUqVKWLlyJbp06QIAuHr1KlxdXbFt2zYEBwfj5MmT8PLyQkJCAho3bgwASEhIgK+vL06dOgVPT89n1nb79m2o1Wrk5OTAysrqZV+iVriN+fM/PT51WttXVAkREdHT/dfPLODpn1sv8vn9n+bE5OTkAABsbW0BACkpKcjIyEBQUJDUxtjYGP7+/oiPjwcAHDp0CA8fPtRo4+LiAm9vb6nNvn37oFarpQADAE2aNIFarZbaEBERUcVm8LIPFEJg+PDheOedd+Dt7Q0AyMjIAAA4OjpqtHV0dMTFixelNkZGRrCxsSnRpujxGRkZcHBwKNGng4OD1OZJ+fn5yM/Pl36+ffv2S74yIiIiUoKXHokZPHgw/v33X6xdu7bEfSqVSuNnIUSJY096sk1p7Z/2PFOnTpUmAavVari6uj7PyyAiIiKFeqkQM2TIEGzZsgW7d+/GG2+8IR13cnICgBKjJZmZmdLojJOTEx48eIDs7Oyntrl27VqJfrOyskqM8hQZO3YscnJypNulS5de5qURERGRQrxQiBFCYPDgwdiwYQN27dqFatWqadxfrVo1ODk5ITo6Wjr24MEDxMbGomnTpgCA+vXrw9DQUKNNeno6kpOTpTa+vr7IycnBgQMHpDb79+9HTk6O1OZJxsbGsLKy0rgRERHR6+uF5sR8/vnnWLNmDTZv3gxLS0tpxEWtVsPU1BQqlQrDhg3DlClTULNmTdSsWRNTpkyBmZkZunXrJrXt27cvRowYATs7O9ja2mLkyJHw8fFBq1atAAC1atVCSEgI+vXrh8WLFwMAPvvsM7Rr1+65rkwiIiKi198LhZiffvoJABAQEKBxfPny5ejVqxcAYPTo0cjLy8OgQYOQnZ2Nxo0bY8eOHbC0tJTaz507FwYGBujcuTPy8vLQsmVLhIeHQ19fX2qzevVqDB06VLqKqX379ggLC3uZ10hERESvof+0Tkx5xnViiIiIXr3XZp0YIiIiIl1hiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRXrhELNnzx689957cHFxgUqlwqZNmzTu79WrF1QqlcatSZMmGm3y8/MxZMgQ2Nvbw9zcHO3bt8fly5c12mRnZyM0NBRqtRpqtRqhoaG4devWC79AIiIiej29cIjJzc3F22+/jbCwsDLbhISEID09Xbpt27ZN4/5hw4Zh48aNiIyMRFxcHO7evYt27dqhoKBAatOtWzckJSUhKioKUVFRSEpKQmho6IuWS0RERK8pgxd9QJs2bdCmTZuntjE2NoaTk1Op9+Xk5GDp0qVYuXIlWrVqBQBYtWoVXF1d8ffffyM4OBgnT55EVFQUEhIS0LhxYwDAkiVL4Ovri9OnT8PT0/NFyyYiIqLXjCxzYmJiYuDg4AAPDw/069cPmZmZ0n2HDh3Cw4cPERQUJB1zcXGBt7c34uPjAQD79u2DWq2WAgwANGnSBGq1WmpDREREFdsLj8Q8S5s2bfDRRx+hatWqSElJwbhx49CiRQscOnQIxsbGyMjIgJGREWxsbDQe5+joiIyMDABARkYGHBwcSjy3g4OD1OZJ+fn5yM/Pl36+ffv2K3xVREREVN688hDTpUsX6b+9vb3RoEEDVK1aFX/++Sc6depU5uOEEFCpVNLPxf+7rDbFTZ06FRMnTvwPlRMREZGSyH6JtbOzM6pWrYqzZ88CAJycnPDgwQNkZ2drtMvMzISjo6PU5tq1ayWeKysrS2rzpLFjxyInJ0e6Xbp06RW/EiIiIipPZA8xN27cwKVLl+Ds7AwAqF+/PgwNDREdHS21SU9PR3JyMpo2bQoA8PX1RU5ODg4cOCC12b9/P3JycqQ2TzI2NoaVlZXGjYiIiF5fL3w66e7duzh37pz0c0pKCpKSkmBrawtbW1tMmDABH3zwAZydnZGamoqvv/4a9vb26NixIwBArVajb9++GDFiBOzs7GBra4uRI0fCx8dHulqpVq1aCAkJQb9+/bB48WIAwGeffYZ27drxyiQiIiIC8BIhJjExEYGBgdLPw4cPBwD07NkTP/30E44dO4YVK1bg1q1bcHZ2RmBgINatWwdLS0vpMXPnzoWBgQE6d+6MvLw8tGzZEuHh4dDX15farF69GkOHDpWuYmrfvv1T16YhIiKiikUlhBC6LkIOt2/fhlqtRk5OTrk/teQ25s//9PjUaW1fUSVERERP918/s4Cnf269yOc3904iIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRWKIISIiIkViiCEiIiJFYoghIiIiRXrhELNnzx689957cHFxgUqlwqZNmzTuF0JgwoQJcHFxgampKQICAnD8+HGNNvn5+RgyZAjs7e1hbm6O9u3b4/LlyxptsrOzERoaCrVaDbVajdDQUNy6deuFXyARERG9nl44xOTm5uLtt99GWFhYqffPmDEDc+bMQVhYGA4ePAgnJye0bt0ad+7ckdoMGzYMGzduRGRkJOLi4nD37l20a9cOBQUFUptu3bohKSkJUVFRiIqKQlJSEkJDQ1/iJRIREdHryOBFH9CmTRu0adOm1PuEEJg3bx6++eYbdOrUCQAQEREBR0dHrFmzBv3790dOTg6WLl2KlStXolWrVgCAVatWwdXVFX///TeCg4Nx8uRJREVFISEhAY0bNwYALFmyBL6+vjh9+jQ8PT1f9vUSERHRa+KVzolJSUlBRkYGgoKCpGPGxsbw9/dHfHw8AODQoUN4+PChRhsXFxd4e3tLbfbt2we1Wi0FGABo0qQJ1Gq11OZJ+fn5uH37tsaNiIiIXl8vPBLzNBkZGQAAR0dHjeOOjo64ePGi1MbIyAg2NjYl2hQ9PiMjAw4ODiWe38HBQWrzpKlTp2LixIn/+TUQUfngNubP//T41GltX1ElRFReyXJ1kkql0vhZCFHi2JOebFNa+6c9z9ixY5GTkyPdLl269BKVExERkVK80hDj5OQEACVGSzIzM6XRGScnJzx48ADZ2dlPbXPt2rUSz5+VlVVilKeIsbExrKysNG5ERET0+nqlIaZatWpwcnJCdHS0dOzBgweIjY1F06ZNAQD169eHoaGhRpv09HQkJydLbXx9fZGTk4MDBw5Ibfbv34+cnBypDREREVVsLzwn5u7duzh37pz0c0pKCpKSkmBra4sqVapg2LBhmDJlCmrWrImaNWtiypQpMDMzQ7du3QAAarUaffv2xYgRI2BnZwdbW1uMHDkSPj4+0tVKtWrVQkhICPr164fFixcDAD777DO0a9eOVyYRERERgJcIMYmJiQgMDJR+Hj58OACgZ8+eCA8Px+jRo5GXl4dBgwYhOzsbjRs3xo4dO2BpaSk9Zu7cuTAwMEDnzp2Rl5eHli1bIjw8HPr6+lKb1atXY+jQodJVTO3bty9zbRoiIiKqeFRCCKHrIuRw+/ZtqNVq5OTklPv5MbwKg6gk/l0QlU//9W8TePrf54t8fnPvJCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUqRXHmImTJgAlUqlcXNycpLuF0JgwoQJcHFxgampKQICAnD8+HGN58jPz8eQIUNgb28Pc3NztG/fHpcvX37VpRIREZGCyTISU7t2baSnp0u3Y8eOSffNmDEDc+bMQVhYGA4ePAgnJye0bt0ad+7ckdoMGzYMGzduRGRkJOLi4nD37l20a9cOBQUFcpRLRERECmQgy5MaGGiMvhQRQmDevHn45ptv0KlTJwBAREQEHB0dsWbNGvTv3x85OTlYunQpVq5ciVatWgEAVq1aBVdXV/z9998IDg6Wo2QiIiJSGFlGYs6ePQsXFxdUq1YNXbt2xYULFwAAKSkpyMjIQFBQkNTW2NgY/v7+iI+PBwAcOnQIDx8+1Gjj4uICb29vqU1p8vPzcfv2bY0bERERvb5eeYhp3LgxVqxYge3bt2PJkiXIyMhA06ZNcePGDWRkZAAAHB0dNR7j6Ogo3ZeRkQEjIyPY2NiU2aY0U6dOhVqtlm6urq6v+JURERFRefLKTye1adNG+m8fHx/4+vqiRo0aiIiIQJMmTQAAKpVK4zFCiBLHnvSsNmPHjsXw4cOln2/fvs0gozBuY/78T49Pndb2FVVCRERKIPsl1ubm5vDx8cHZs2eleTJPjqhkZmZKozNOTk548OABsrOzy2xTGmNjY1hZWWnciIiI6PUle4jJz8/HyZMn4ezsjGrVqsHJyQnR0dHS/Q8ePEBsbCyaNm0KAKhfvz4MDQ012qSnpyM5OVlqQ0RERPTKTyeNHDkS7733HqpUqYLMzEx8//33uH37Nnr27AmVSoVhw4ZhypQpqFmzJmrWrIkpU6bAzMwM3bp1AwCo1Wr07dsXI0aMgJ2dHWxtbTFy5Ej4+PhIVysRERERvfIQc/nyZXz88ce4fv06KlWqhCZNmiAhIQFVq1YFAIwePRp5eXkYNGgQsrOz0bhxY+zYsQOWlpbSc8ydOxcGBgbo3Lkz8vLy0LJlS4SHh0NfX/9Vl0tEREQK9cpDTGRk5FPvV6lUmDBhAiZMmFBmGxMTEyxYsAALFix4xdURERHR64J7JxEREZEiMcQQERGRIjHEEBERkSIxxBAREZEiybIBJBER0av0X1f0Briq9+uIIzFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgMMURERKRIDDFERESkSAwxREREpEgGui6AqDxxG/Pnf3p86rS2r6gSIiJ6Fo7EEBERkSIxxBAREZEiMcQQERGRIjHEEBERkSIxxBAREZEiMcQQERGRIjHEEBERkSIxxBAREZEiVejF7v7rwmbA67O4GRd5IyIipanQIYaI6Gn4RYeofOPpJCIiIlIkhhgiIiJSJJ5OIqISOEeKiJSAIzFERESkSByJISIiUghONtfEkRgiIiJSpHI/ErNw4ULMnDkT6enpqF27NubNm4fmzZvruiwiIq3gN2+ispXrELNu3ToMGzYMCxcuRLNmzbB48WK0adMGJ06cQJUqVXRdHpEsOKmWyhsGKSqvynWImTNnDvr27YtPP/0UADBv3jxs374dP/30E6ZOnarj6oiIqCJhmCt/yu2cmAcPHuDQoUMICgrSOB4UFIT4+HgdVUVERETlRbkdibl+/ToKCgrg6OiocdzR0REZGRkl2ufn5yM/P1/6OScnBwBw+/btMvsozL/3n+t82vM/r/9aB2tgDayhfNbwKupgDayhotVQdJ8Q4tlPJMqpK1euCAAiPj5e4/j3338vPD09S7QfP368AMAbb7zxxhtvvL0Gt0uXLj0zK5TbkRh7e3vo6+uXGHXJzMwsMToDAGPHjsXw4cOlnwsLC3Hz5k3Y2dlBpVK9VA23b9+Gq6srLl26BCsrq5d6jv+KNbAG1sAaWEP5r6G81PE61CCEwJ07d+Di4vLMtuU2xBgZGaF+/fqIjo5Gx44dpePR0dHo0KFDifbGxsYwNjbWOGZtbf1KarGystLpHwZrYA2sgTWwBmXUUF7qUHoNarX6udqV2xADAMOHD0doaCgaNGgAX19f/Pzzz0hLS8OAAQN0XRoRERHpWLkOMV26dMGNGzcwadIkpKenw9vbG9u2bUPVqlV1XRoRERHpWLkOMQAwaNAgDBo0SCd9GxsbY/z48SVOU7EG1sAaWANrYA3lsY6KVoNKiOe5homIiIiofCm3i90RERERPQ1DDBERESkSQwwREREpUrmf2EtEQFZWFk6fPg2VSgUPDw9UqlRJ1yUREekcR2KIyrHc3Fz06dMHLi4u8PPzQ/PmzeHi4oK+ffvi3r3/vn8JESlbVFQU4uLipJ9//PFH1KlTB926dUN2drYOK9MOXp1UTp04cQJpaWl48OCBxvH27dvrqCLShf79++Pvv/9GWFgYmjVrBgCIi4vD0KFD0bp1a/z00086rrDiuXz5MrZs2VLq3+ecOXNk7z83NxfTpk3Dzp07kZmZicLCQo37L1y4IHsNAFBQUIBNmzbh5MmTUKlUqFWrFjp06AB9fX2t9A8AZ86cQUxMTKn/Dt99953W6iiuoKAAx44dQ9WqVWFjYyN7fz4+Ppg+fTreffddHDt2DA0bNsTw4cOxa9cu1KpVC8uXL5e9huLy8vLw8OFDjWNyrhzMEFOMjY1NqfssqVQqmJiYwN3dHb169ULv3r1lq+HChQvo2LEjjh07BpVKJe3iWVRXQUGBbH0/KTc3F7GxsaW+WQ8dOlSWPn/44Qd89tlnMDExwQ8//PDUtnLV8KTY2FjMmjVL48161KhRaN68uex929vb4/fff0dAQIDG8d27d6Nz587IysqSvQYAuHbtGkaOHCl9cD75tqHN30td2rlzJ9q3b49q1arh9OnT8Pb2RmpqKoQQqFevHnbt2iV7DR9//DFiY2MRGhoKZ2fnEu9ZX3zxhew1nDt3Dm3btsXly5fh6ekJIQTOnDkDV1dX/Pnnn6hRo4bsNSxZsgQDBw6Evb09nJycNP4dVCoVDh8+LHsNADBs2DD4+Pigb9++KCgogL+/P+Lj42FmZoY//vijxN/uq2ZhYYHk5GS4ublhwoQJSE5Oxu+//47Dhw/j3XffLbH/oBzu3buH0aNH49dff8WNGzdK3C/r+8N/22v69TJnzhxhZ2cnevToIX744Qcxf/580aNHD2Fvby/+97//iU8//VQYGxuLn3/+WbYa2rVrJzp06CAyMzOFhYWFOHHihPjnn39Eo0aNxJ49e2Tr90mHDx8WTk5OwsrKSujr64tKlSoJlUolzM3NRbVq1WTr183NTVy/fl3677JuctZQ3MqVK4WBgYHo3LmzmD9/vpg3b57o3LmzMDQ0FKtXr5a9f1NTU3HixIkSx5OTk4WZmZns/RcJCQkRXl5eYuHChWLjxo1i06ZNGjdtefTokZg5c6Zo2LChcHR0FDY2Nho3uTVs2FCMGzdOCCGEhYWFOH/+vLhz545o3769WLhwoez9CyGEWq0WcXFxWumrLG3atBEhISHixo0b0rHr16+LkJAQ8e6772qlhipVqohp06Zppa+nqVy5sjh48KAQQoiNGzcKFxcXcfr0afHNN9+Ipk2byt6/jY2NOH78uBBCiGbNmonFixcLIYRISUkRpqamsvcvhBCDBg0StWrVEr/99pswNTUVy5YtE5MnTxZvvPGGWLVqlax9M8QU06lTJ/HTTz+VOL5o0SLRqVMnIYQQP/zwg/D29patBjs7O3H06FEhhBBWVlbi1KlTQgghdu7cKerUqSNbv0/y9/cX/fr1E48ePZLerNPS0oSfn59Yv3691urQtTfffFPMmTOnxPHZs2eLN998U/b+W7RoIT766CORl5cnHbt375746KOPRMuWLWXvv4iFhYU4cuSI1vory7hx44Szs7OYOXOmMDExEZMnTxZ9+/YVdnZ2Yv78+bL3b2FhIc6dOyeEEMLa2lokJycLIYRISkoSVatWlb1/IR6H+9KCrTaZmZmJf//9t8TxpKQkYW5urpUaLC0txfnz57XS19MYGxuLS5cuCSGE6Nevn/jiiy+EEEJcuHBBWFpayt7/e++9J4KDg8WkSZOEoaGhuHz5shBCiO3bt4uaNWvK3r8QQri6uordu3cLIR7/fzl79qwQQogVK1aINm3ayNo3Q0wx5ubm0j9+cWfPnpX+MM+dOyfrN2Bra2vpD7N69epi165dUr/aStVCPP62VxSg1Gq19KaZkJAgPD09tVaHrhkZGZX5O2FsbCx7/8eOHROVK1cWdnZ2okWLFqJly5bCzs5OVK5cWfoA1YZatWqJw4cPa62/slSvXl388ccfQgjNQDF//nzx8ccfy96/o6Oj9K3Xy8tLbN68WQih3Q/vlStXig8//FDk5uZqpb/S2NjYiL1795Y4HhcXp5URMSGE6NOnT6lfOrWtSpUqYvv27eLRo0fC1dVVbN26VQjxeLTU2tpa9v4vXrwo2rZtK9566y3xyy+/SMeHDRsmhgwZInv/Qjz+7ExNTRVCPB6Z2r9/vxDicZCT+++Cl1gXY2tri61bt+LLL7/UOL5161bY2toCeDxPxNLSUrYavL298e+//6J69epo3LgxZsyYASMjI/z888+oXr26bP0+ydDQUDrH7OjoiLS0NNSqVQtqtRppaWmy9Tt8+HBMnjwZ5ubmGD58+FPbamMSpaurK3bu3Al3d3eN4zt37oSrq6vs/Xt7e+Ps2bNYtWoVTp06BSEEunbtiu7du8PU1FT2/ovMmzcPY8aMweLFi+Hm5qa1fp+UkZEBHx8fAI/nAuTk5AAA2rVrh3Hjxsnef5MmTbB37154eXmhbdu2GDFiBI4dO4YNGzagSZMmsvcPALNnz8b58+fh6OgINzc3GBoaatyvjbkg7dq1w2effYalS5eiUaNGAID9+/djwIABWrv4wN3dHePGjUNCQgJ8fHxK/Dtoa85c79690blzZ2l+UuvWrQE8/vd48803Ze+/SpUq+OOPP0ocnzt3rux9F6levTpSU1NRtWpVeHl54ddff0WjRo2wdetWWFtby9o3Q0wx48aNw8CBA7F79240atQIKpUKBw4cwLZt27Bo0SIAQHR0NPz9/WWr4dtvv0Vubi4A4Pvvv0e7du3QvHlz2NnZITIyUrZ+n1S3bl0kJibCw8MDgYGB+O6773D9+nWsXLlS+hCRw5EjR6SZ7UeOHCmzXWkTsOUwYsQIDB06FElJSWjatClUKhXi4uIQHh6O+fPna6UGU1NT9OvXTyt9FffkRPfc3FzUqFEDZmZmJT4wbt68qZWa3njjDaSnp6NKlSpwd3fHjh07UK9ePRw8eFArm83NmTMHd+/eBQBMmDABd+/exbp16+Du7q61D433339fK/08zQ8//ICePXvC19dX+l149OgR2rdvr7W/i59//hkWFhaIjY1FbGysxn0qlUprIWbChAnw9vbGpUuX8NFHH0m/h/r6+hgzZoxWaiii7SuDivTu3RtHjx6Fv78/xo4di7Zt22LBggV49OiR7F82eXXSE/bu3YuwsDCcPn0aQgi8+eabGDJkCJo2baqzmm7evCldqqetD+/ExETcuXMHgYGByMrKQs+ePREXFwd3d3csX74cb7/9tlbqKA82btyI2bNn4+TJkwAgXZ3UoUMH2fueOnUqHB0d0adPH43jy5YtQ1ZWFr766ivZ+o6IiHjutj179pStjuLGjBkDKysrfP311/j999/x8ccfw83NDWlpafjyyy8xbdo0rdRBj509e1YaIfTy8ioxYknyy83NxVdffaWbK4PKkJaWhsTERNSoUUP2zwqGmHJm6tSpGDt2bInjhYWF6N69O9auXauDqkhX3NzcsGbNmhIhev/+/ejatStSUlJ0VFn5kJCQgPj4eLi7u2t9DaW7d++WWJtEG996ixw6dEi67N/Lywt169bVWt/ljXhiKQq5laelID7//HPs3r0bkyZNwieffIIff/wRV65cweLFizFt2jR0795d1v51jSHmCYWFhTh37lypiyf5+fnJ3r+joyMmT56Mzz77TDpWUFCArl27Ijk5WRoNqCgOHjyI3377rdS1ajZs2KC1Oh48eFDq70SVKlVk7dfExAQnT55EtWrVNI5fuHABXl5euH//vqz9l0ZXQ9blQUpKCgYPHoyYmBiNf3shBFQqlVa+9WZmZqJr166IiYmBtbU1hBDIyclBYGAgIiMjZduS4llz1IrTxnw1AFixYgVmzpyJs2fPAgA8PDwwatQohIaGytpvtWrVkJiYCDs7uxJ/m8WpVCrZFx+sUqUKVqxYgYCAAFhZWeHw4cNwd3fHypUrsXbtWmzbtk2WfstLkOOcmGISEhLQrVs3XLx4scRiXtp6g9q2bRtatWoFa2trdO7cGQ8fPkSXLl1w6tQp7N69W/b+i5SHxc0iIyPxySefICgoCNHR0QgKCsLZs2eRkZGBjh07yt4/8Hi4vE+fPoiPj9c4rq0PLVdXV+zdu7fEG+XevXvh4uIia9/F6XLIesuWLc/dVu7RmKJvtcuWLYOjo6PWvvkXN2TIENy+fRvHjx9HrVq1ADxe4btnz54YOnSobKO1T85RO3ToEAoKCuDp6Qng8eq5+vr6qF+/viz9P2nOnDkYN24cBg8ejGbNmkEIgb1792LAgAG4fv16iQs0XqXiI6C6Hg29efOm9P5gZWUlzU975513MHDgQNn6nTt3Lrp37w4TE5OnzgeTfX6SrNc+Kczbb78tPvroI3HixAmRnZ0tbt26pXHTlt27dwsrKyuxadMm8d577wkvLy+RkZGhtf6FKB+Lm/n4+IiwsDAhxP8tLFZYWCj69esnvvvuO63U0LRpU+Hn5ye2bdsmjhw5IpKSkjRucps2bZqws7MTy5YtE6mpqSI1NVUsXbpU2NnZiSlTpsjefxFdLmalUqk0bnp6eqUe09PTk7UOIR5fSlq09ICuWFlZiQMHDpQ4vn//fqFWq7VSw+zZs8V7770nbt68KR27efOm6NChg5g1a5ZWanBzcxMREREljoeHhws3Nzet1PCkwsJCUVhYqNU+fXx8RExMjBBCiNatW4sRI0YIIR4vO1C5cmWt1qILDDHFmJmZlbomiC5s3rxZGBgYCB8fH5GVlaX1/svD4mZmZmYiJSVFCPF4EcCixbVOnDghnJyctFbDyZMntdJXaQoLC8Xo0aOFiYmJ9EFtZmYmJk6cqNU6dLmYVXHR0dGiXr16IioqSuTk5Ijbt2+LqKgo0aBBA7Fjxw7Z+w8ICBDR0dGy9/M0Zf1tHj58WCuLqwkhhIuLS6nrFB07dkw4OztrpQZjY+NS36/PnDmjlTWciouIiBDe3t7C2NhYGBsbCx8fH7FixQqt9D1nzhxpocddu3YJU1NTYWRkJPT09MS8efO0UoMu8XRSMY0bN8a5c+e0PsO+U6dOpR6vVKkSrK2tNebHaGseiKura4lTSNpma2uLO3fuAAAqV66M5ORk+Pj44NatW1rbwdnLywvXr1/XSl+lUalUmD59OsaNG4eTJ0/C1NQUNWvW1MrlxMXpasj6ScOGDcOiRYvwzjvvSMeCg4NhZmaGzz77TPY5Y7/88gsGDBiAK1euwNvbu8Sl5m+99Zas/QNAixYt8MUXX2Dt2rXSKcUrV67gyy+/RMuWLWXvHwBu376Na9euoXbt2hrHMzMzpb9Zubm7u+PXX3/F119/rXF83bp1qFmzplZqAHR7WguAxvMHBgbi1KlTWrsyqEhBQQHCw8PL3JRUzj3FGGKKGTJkCEaMGCEtqKWtNyi1Wl3q8eDgYFn6ex7lYXGz5s2bIzo6Gj4+PujcuTO++OIL7Nq1C9HR0bK+Wd++fVv67+nTp2P06NGYMmVKqb8T2prQamFhgYYNG2qlr9LocjGr4s6fP1/q34tarUZqaqrs/WdlZeH8+fMam8AWbdSqrXlzYWFh6NChA9zc3ODq6gqVSoW0tDT4+Phg1apVsvcPAB07dkTv3r0xe/ZsaZG/hIQEjBo1qswvZa/axIkT0aVLF+zZswfNmjWT1nDauXMnfv31V63UAAALFizATz/9hE8++UQ61qFDB9SuXRsTJkyQPcQ8qUqVKtIFB/fu3YOZmZnsfX7xxRcIDw9H27Zt4e3trdW5Yrw6qRg9Pb0Sx7T9BlVe2NjY4N69e3j06JHOFje7efMm7t+/DxcXFxQWFmLWrFnSWjXjxo2TbZt7PT09jT/Cov//xcn5O9GpUyeEh4fDysrqmR8I2hqZmzt3LvT19TF06FDs3r0bbdu2RUFBgbSYlTZ2TgYeXyFoaGiIVatWwdnZGcDjVXxDQ0Px4MGDEouevWpeXl6oVasWRo8eXerE3qpVq8raf3HR0dEaa7S0atVKa33fu3cPI0eOxLJly6Qr1QwMDNC3b1/MnDkT5ubmWqnj0KFDmDt3Lk6ePCn9O4wYMUKrl5ubmJggOTm5xAj+2bNn4ePjI/sVhAEBAVi1ahXeeOMNjeMHDhxAjx49cObMGVn7BwB7e3usWLEC7777rux9PYkhppiLFy8+9X5tvEHl5eVBCCGl54sXL2Ljxo3w8vJCUFCQ7P0XedZCZ9pa3EwXXuSDUI7Vm3v37o0ffvgBlpaWGt/4S7N8+fJX3v/z0OZiVsWdO3cOHTt2xOnTp6Vvm2lpafDw8MCmTZtkPxVsbm6Oo0ePclG3/y83Nxfnz5+HEALu7u5aCy/libe3N7p161bitNb333+PdevW4dixY7L23759e8TFxWHhwoXo2rUrCgsLMWnSJEydOhVDhgzBrFmzZO0fAFxcXBATEwMPDw/Z+3oSQ0w5ExQUhE6dOmHAgAG4desWPD09YWRkhOvXr2POnDlanX+ga927d0dAQAD8/f118sdB/2fFihXo0qVLibk4Dx48kC6F1xYhRKmjENoYwn7vvffQq1cvfPDBB7L3VZay1uRQqVQwMTGBu7s7/Pz8oK+vr+XKtEtfXx/p6elwcHDQOH7jxg04ODhobeR8/fr16NKlC1q1alXqaS1tLAexaNEijBw5Eu3bt0dqairS0tIQHh6utdG52bNn48KFCwgLC9P6sgMVPsRs2bIFbdq0gaGh4TPXo9DGiqD29vaIjY1F7dq18csvv2DBggU4cuQI1q9fj++++04ni93panGz/v37IzY2FmfOnIGTkxP8/f3h7++PgIAArWysVty9e/dKXXBP7omcx48fLzF5skhUVBRCQkJk7b9IefnA0LWff/4Z33//Pfr06VPqHCltvEdUq1YNWVlZuHfvHmxsbCCEwK1bt2BmZgYLCwtkZmaievXq2L17t6yblOp6IUo9PT1kZGSU+J28evUqatSogby8PNlrKFIeTmuNHTsW06dPh4GBAWJiYrS6VU7Hjh2xe/du2Nraonbt2iX+LuT8fajwIab4H0Jpc2KKaGtOjJmZGU6dOoUqVaqgc+fOqF27NsaPH49Lly7B09NTa1fllKf9ODIyMhATE4OYmBgp1Dg4OCA9PV32vrOystC7d2/89ddfpd4v97+DqakpZsyYgSFDhkjH8vPzMWLECCxdulRrb9R6enq4du1aidVgjx49isDAQK1tAAk8/t2MjY0t9cNT7iXey8N7xNq1a/Hzzz/jl19+QY0aNQA8Ps3Wv39/fPbZZ2jWrBm6du0KJycn/P7777LU8KyFKOU8zVk0EvXll19i8uTJsLCwkO4rKCjAnj17kJqa+tQNZF8n2dnZ+PTTT7Fz507MnDkTsbGx2LRpE2bMmIFBgwZppQadnvbW3tXc9Dx8fHzE/PnzRVpamrCyshLx8fFCCCESExOFo6Oj1urQ5eJmT7p7966IiooSY8aMEU2aNBFGRkaiTp06Wum7W7duomnTpuLAgQPC3Nxc7NixQ6xcuVJ4enqKP/74Q/b+169fL+zs7ERISIhIT08XR44cEbVq1RK1atUShw4dkr3/OnXqiLp16wo9PT3h4+Mj6tatK93eeustYWlpKT766CPZ6yhy+PBh4eTkJKysrIS+vr6oVKmSUKlUwtzcXFSrVk1rdehS9erVy1wnpujfYO/evbKupaTLhSjd3NyEm5ubUKlUwtXVVfrZzc1NeHh4iKCgIJGQkCBrDU8qKCgQp0+fFv/884+IjY3VuMnNxcVFNGvWTFy4cEE6FhkZKWxtbcW7774re/8PHz4U4eHhIj09Xfa+SsMQU8789ttvwtDQUOjp6YnWrVtLx6dMmSJCQkK0Vkd5WNxs9OjRonHjxsLExEQ0aNBADB8+XGzevFlkZ2drpX8hhHBychL79+8XQjz+dzh9+rQQ4vFihM2aNdNKDVeuXBGtWrUSdnZ2wsTERAwcOFDcu3dPK31PmDBBjB8/XqhUKjFy5EgxYcIE6TZlyhSxZs0akZ+fr5VahBDC399f9OvXTzx69Ej68ExLSxN+fn5i/fr1WqtDl0xNTcXBgwdLHD9w4IAwNTUVQgiRkpIizM3NZauhPCxEGRAQoLFisK7s27dPVKtWrcyVpOU2adIkUVBQUOL4pUuXRKtWrWTvX4jHv5Opqala6etJXCfmCQcOHEBMTEypC/ZoY1OzDz/8EO+88w7S09M1rvpo2bKl1vYLAsrH4mYzZ85EpUqVMH78eHTo0EHaJ0abcnNzpXPutra2yMrKgoeHB3x8fHD48GGt1FBQUIAHDx6goKAABQUFcHJy0tpid+PHjwfweJ2Y0ib2altSUhIWL14MfX196OvrIz8/H9WrV8eMGTPQs2dPraxREhsbi1mzZkk7SNeqVQujRo1C8+bNZe8beLygWf/+/fHLL79Icy6OHDmCgQMHokWLFgCAY8eOPXVjwv+qPCxEqc295J5mwIABaNCgAf788084OztrfWLruHHjpP++f/8+TExMAABvvPEGoqOjtVJD48aNceTIEa0uMVCEIaaYKVOm4Ntvv4Wnp2eJNSC0+Yvp5OQEJycnjWONGjXSWv9A+Vjc7MiRI4iNjUVMTAxmz54NfX19aWJvQECAVkKNp6cnTp8+DTc3N9SpU0da/G/RokXSOiVyioyMxMCBA9G8eXOcOXMGSUlJ6N27N7Zv346VK1eievXqsvZffM2c4ue9rays4OnpidGjR2ttcTMAMDQ0lOpxdHREWloaatWqBbVajbS0NNn7X7VqFXr37o1OnTph6NChEEIgPj4eLVu2RHh4OLp16yZ7DUuXLkVoaCjq168vTaB89OgRWrZsiaVLlwJ4vDji7NmzZatBVwtRFlfWrtrFr9Lq0KEDbG1tZa3j7Nmz+P3333V22X1hYSH+97//YdGiRbh27RrOnDmD6tWrY9y4cXBzc0Pfvn1lr2HQoEEYMWIELl++jPr165e41F7WCyB0Mv5TTjk4OIjly5frugxx4MABMWrUKNGlSxfRsWNHjZu2lMf9OJKSkkSvXr2EgYGBVoZphRBi1apV0u/E4cOHRaVKlYSenp4wMTERkZGRsvdvZmYmFi5cqHHs5s2b4qOPPtLKPjmlbf65adMmER4eLgYNGiRMTU3Fr7/+KnsdRVq3bi1Wr14thBCif//+olGjRmLVqlUiODhYNGrUSPb+33zzTTFnzpwSx2fPni3efPNN2fsv7uTJk2Lz5s1i06ZNWt+U8saNG+LKlStCiMfzQaZPny7ee+898eWXX2rtFE9AQICwsrIS5ubmol69eqJu3brCwsJCqNVq0bhxY2FtbS1sbGzE8ePHZa0jMDBQ/PXXX7L28TQTJ04U1atXF6tWrRKmpqbi/PnzQggh1q1bJ5o0aaKVGp48jVZ8o1a536sZYopxcnISZ86c0WkNa9euFYaGhqJt27bCyMhItGvXTnh6egq1Wi169eqls7ouXrwo1q9fr5Wdm4s7fPiwmDNnjmjfvr2wsbER+vr6on79+mLkyJFaraNIbm6uOHTokNY25Xzah5O2Nph7mrCwMK2EhyIHDx4Uu3btEkIIkZmZKdq0aSMsLS1F3bp1tfK7aWRkVOqmg2fPntX6poP5+fni1KlT4uHDh1rtV9cTOYvMnTtXdOrUSeTk5EjHcnJyxIcffijmzZsncnNzRYcOHURQUJCsdWzYsEF4eXmJ5cuXi8TERHH06FGNm9xq1Kgh/v77byHE/02yFuJxyLW2tpa9fyGESE1NfepNTgwxxUyfPl188cUXOq1Bl7P+i4uIiBD3798vcTw/P19ERERopQZra2thYGAg6tevL0aMGCG2bt2q8YZVUTx8+FBER0eLRYsWidu3bwshHk/2vXPnjo4re7xjsLbeKMuDGjVqiEWLFpU4vmjRIuHu7q6VGnJzc0WfPn2Evr6+0NfXlz60hgwZIqZOnaqVGnQ5kbOIi4tLqaMsycnJwsXFRQghxKFDh4SdnZ2sdehyFEIIIUxMTKT/F8VDzPHjx2Wd3F1ecE5MMSNHjkTbtm1Ro0YNeHl5aXXBniLnz59H27ZtAQDGxsbIzc2FSqXCl19+iRYtWmDixImy1wA8nv8QEhJSYiGpO3fuoHfv3lpZoXXlypXw8/PT2iaLRco6114auSd7X7x4ESEhIUhLS0N+fj5at24NS0tLzJgxA/fv38eiRYtk7f9Z8vLypImE2jBx4kT06NFDWh9F20aMGIGhQ4ciKSkJTZs2lVZnDQ8Px/z587VSw9ixY3H06FHExMRoLHbYqlUrjB8/HmPGjJG9Bl1O5CySk5ODzMxMeHl5aRzPysqSNnG1trYusZbQq5aSkiLr8z9L7dq18c8//5T4f/Hbb79pdbG98+fPY968eRoT3r/44gvZ/1YZYooZMmQIdu/ejcDAQNjZ2Wl9ljlQPmb9A6VveggAly9fLnPX7VetXbt2WunnSU8uknXo0CEUFBTA09MTAHDmzBno6+ujfv36stfyxRdfoEGDBjh69Cjs7Oyk4x07dsSnn34qe//PsmTJEq2+Ua5fvx6TJk1Cw4YN0aNHD3Tp0qXEAnxyGjhwIJycnDB79mxpp+RatWph3bp16NChg1Zq2LRpE9atW4cmTZpo/I16eXnh/PnzWqlBpxM5/78OHTqgT58+mD17Nho2bAiVSoUDBw5g5MiReP/99wE8vtpU7i1LdBXk+vTpg/nz52P8+PEIDQ3FlStXUFhYiA0bNuD06dNYsWIF/vjjD63Usn37drRv3x516tRBs2bNpAnvtWvXxtatW9G6dWvZ+q7wK/YWZ2lpicjISGkkRBe6deuGBg0aYPjw4fjf//6H+fPno0OHDoiOjka9evVkHw2qW7cuVCoVjh49itq1a8PA4P9ybkFBAVJSUhASEqK1re51vbT5nDlzEBMTg4iICGnX7OzsbPTu3RvNmzfHiBEjZO3f3t4ee/fuhaenJywtLXH06FHpyjEvLy/Zg21Zo1I5OTlITEzE+fPn8c8//2g1yBw/fhyrV69GZGQkLl++jFatWqFHjx54//33pY1TX2dmZmZITk5G9erVNX4njh49Cj8/P+Tk5MheQ2krF6tUKll3d3/S3bt38eWXX2LFihV49OgRgMc7affs2RNz586Fubk5kpKSAAB16tR5pX2Xh+1qim8Fsn37dkyZMgWHDh1CYWEh6tWrh++++05rmwbXrVsXwcHBmDZtmsbxMWPGYMeOHbIuR8EQU0zVqlWxfft2re/LU9zNmzdx//59uLi4oLCwELNmzUJcXBzc3d0xbtw46YNULkWnqyZOnIgRI0ZoLOltZGQENzc3fPDBBzAyMpK1DkC3S5sXqVy5Mnbs2FFi/6Lk5GQEBQXh6tWrsvZva2uLuLg4eHl5aXxgxcXF4YMPPsC1a9dk7T8wMLDU41ZWVnjzzTcxaNAgnZ5S2Lt3L9asWYPffvsN9+/fl04jyO3BgwelriVVtLO2nPz9/fHhhx9iyJAhsLS0xL///otq1aph8ODBOHfuHKKiomSv4eLFi0+9X5u/E3fv3sWFCxcghECNGjU03rPkUh62qylr7yhdMDExwbFjx1CzZk2N42fOnMFbb72F+/fvy9e57qbjlD/Lli0TnTt3Frm5ubouRefCw8NFXl6eTmsoD5OcLSwsxM6dO0sc37lzp7CwsJC9/86dO4t+/fpJtVy4cEHcuXNHtGjRQqdXq5UXR44cESNGjBCVK1cWJiYmsvd35swZ8c477wg9PT2Nm7YmcQrxeEsBS0tLMWDAAGFiYiK++OIL0apVK2Fubi4SExO1UgPpnkqlEpmZmbouQwghxBtvvFHqUgvr1q0Trq6usvbNkZhi6tati/Pnz0MIATc3txITe+UaEnuRb4/anuSqy2+c5ubmOH78ONzc3GBvb4/du3fDx8cHJ0+eRIsWLbSyAeQnn3yC2NhYzJ49G02aNAEAJCQkYNSoUfDz80NERISs/V+9ehWBgYHQ19fH2bNn0aBBA5w9exb29vbYs2dPufgWpm0pKSlYs2YNVq9ejTNnzsDPzw/dunXDRx99JPt8rWbNmsHAwABjxowpdXXW4qtsyyk5ORkzZ87UOH3w1VdfwcfHRyv9A7qbyFkkNzcX06ZNw86dO0t9j7pw4YJW6tAVPT09qNXqZ87d1MbmrJMmTcLcuXMxZswYjQnv06dPx4gRI/Dtt9/K1jcn9hZTNBlM26ytrZ/5iyi0eK4ZeLwKZZ8+fRAfH6+zOsrDJOdFixZh5MiR6NGjBx4+fAghBAwNDdG3b1/MnDlT9v5dXFyQlJSEtWvX4vDhwygsLETfvn3RvXt3mJqayt5/eePr64sDBw7Ax8cHvXv3Rrdu3VC5cmWt9Z+UlIRDhw7p7JTzw4cP8dlnn2HcuHGyB+in0eVEziKffvopYmNjERoaqvXl/ot20n4ecu6sPnHiRK1daPE048aNg6WlJWbPno2xY8cCePzeNWHCBNl3ludITDkQGxv7XO2OHDmCYcOGyVvM/1cevnHqepJzcbm5udIonbu7e4mrMUg7vv76a3Tv3r3EHCVtadiwIebOnYt33nlHJ/0Dj7/0HD58WPYtJ55GlxM5i1hbW+PPP/9Es2bNZO/rSc+7L5VKpZJtREjXc2KKT24uruiLp6WlpVbqYIgpxaFDh6QhUi8vL61eefGknJwcrF69Gr/88guOHj2qtZEYc3NznX7jBHQ7yfl59wOSK0jt2bPnudr5+fnJ0n959+DBA6SkpKBGjRoaV9DJofjp3sTERHz77beYMmUKfHx8SryBa+N0b+/eveHj4/NC6xm9ajqdyPn/VatWDdu2bdPJxrDlQfGrk3TVf0ZGBipVqqTTWng6qZjMzEx07doVMTExsLa2hhACOTk5CAwMRGRkpFbXo9i1axeWLVuGDRs2oGrVqvjggw+kzd20wcvLC9evX9daf8UVfWgYGBjAwsJC+nnAgAEYMGCAVmrQ9RBtQECANPpV1vcMbZ5eLC/y8vIwePBg6VRK0WZ3Q4cOhYuLiywLvT15ulcIUWKTQ22eZnV3d8fkyZOxd+9eNGjQoMSooNzD9wBQqVIlJCUllQgxSUlJWvsgmzx5Mr777jtERERUiEvrn6Tr8YdKlSohISEB7733XpnrimkDR2KK6dKlC86fP4+VK1dK6f7EiRPo2bMn3N3dsXbtWln7v3z5MsLDw7Fs2TLk5uaic+fOWLRoEY4ePVpiVUq57dq1S2ffOIvvnPw0r/MHuJ2dHSwtLdGrVy+EhobC3t6+1Ha6Dlva9sUXX2Dv3r2YN28eQkJC8O+//6J69erYsmULxo8fX2KhwlfheU/3Ao8vf5bb005lyHn6ojhdTuQsoqsLMYDytaq3rkyYMAGTJk3S+Xs1Q0wxarUaf//9Nxo2bKhx/MCBAwgKCsKtW7dk6/vdd99FXFwc2rVrh+7duyMkJAT6+vowNDTUSYgpWvvgyV9QbXzjLP6hIYTAu+++i19++aXEBE5tfGDoyoMHD7Bx40YsW7YM//zzD95991307dsXISEhOvvGUx5UrVpVWq22+Lo5586dQ7169bS2Tkx5cP36dahUKo2VnLVFCIF58+Zh9uzZ0lpJLi4uGDVqFIYOHaqV39FnbcEyfvx42foua/2k0uzevVu2OnTt1KlTOHfuHNq3b4/ly5fD2tq61HZyrmbNEFOMpaUl/vnnnxKrOx45cgT+/v6yvkEaGBhg6NChGDhwoMYQra5CzLO+fWozQBT/sKqILl26hOXLlyMiIgL5+fno2bMnJk6cKPtckPKoPKxWCwD37t0rdRVpuZfbv3XrFr755husW7cO2dnZAAAbGxt07doV//vf/3QyMqftiZxU/kycOBGjRo3SyWk9hphiOnTogFu3bmHt2rVwcXEBAFy5cgXdu3eHjY0NNm7cKFvf+/btw7Jly/Drr7/izTffRGhoKLp06QIXFxedhJjypKKHmCIpKSno27cvYmNjkZWVBVtbW12XpHW6Xq02KysLvXv3xl9//VXq/XKOUN68eRO+vr7Se1KtWrUghMDJkyexZs0auLq6Ij4+XvZVvemx55n8r1KpsH79ei1UU3FVvK9yTxEWFoYOHTrAzc0Nrq6uUKlUSEtLg4+PD1atWiVr376+vvD19cX8+fMRGRmJZcuWYfjw4SgsLER0dDRcXV21/k3n1q1bWLp0qcaVWn369Klw8zB0KT8/H+vXr8eyZcuwb98+tG3bFn/++WeFDDAAMHXqVISEhODEiRN49OgR5s+fj+PHj2Pfvn0vNHflZQ0bNgzZ2dlISEhAYGAgNm7ciGvXruH777/H7NmzZe170qRJMDIywvnz5+Ho6FjivqCgIGmuihyK9lV7HnLNR7G1tcWZM2dgb28PGxubp9Yj9yJvFf19sF69eti5cydsbGye+bvBvZO0LDo6GqdOnYIQAl5eXmjVqpVO6jh9+jSWLl2KlStX4tatW2jduvUzNxt7VRITExEcHAxTU1M0atQIQggkJiYiLy8PO3bsQL169bRSBwCNb9wVxYEDB7B8+XJERkaiWrVq6NWrF3r06FFhw0txx44dw6xZs3SyWq2zszM2b96MRo0awcrKComJifDw8MCWLVswY8YMxMXFyda3m5sbFi9ejODg4FLvj4qKwoABA5CamipL/8+ag1KcXPNRIiIi0LVrVxgbGz9zsb+ePXvKUgM9VvwUki7nJzHEFJOSklIuPygLCgqwdetWLFu2TGshpnnz5nB3d8eSJUukuRePHj3Cp59+igsXLjz3OiYv48lh2q1bt6JFixYlLiXV5mJ32qanp4cqVaqgZ8+eqF+/fpnt5Nohl0pnZWWFf//9F25ubnBzc8Pq1avRrFkzpKSkoHbt2rKuJG1sbIzz58/jjTfeKPX+y5cvw93dXStrtBCVFzydVIy7uzv8/PzQt29ffPjhhzAxMdF1SQAeLyr0/vvva3VbhMTERI0AAzyefDx69Gg0aNBA1r6fHKbt0aOHrP2VV2lpaZg8eXKZ91ekdWKe57J7lUqFR48eyVqHp6cnTp8+DTc3N9SpUweLFy+Gm5sbFi1aBGdnZ1n7tre3R2pqapkhJiUlRetXKul6YdDCwkKcO3eu1L2TKupCkBUNR2KKSU5OxrJly7B69Wrk5+ejS5cu6NOnDxo3bqzr0rTO0dERK1euRFBQkMbx7du345NPPsG1a9d0VBlVRJs3by7zvvj4eCxYsABCCOTl5clax+rVq/Hw4UP06tULR44cQXBwMG7cuAEjIyOEh4ejS5cusvXdt29fnDt3DtHR0TAyMtK4Lz8/H8HBwahRo4ZWFsUsDwuDJiQkoFu3brh48WKJhd8qUsDXlWfNSSpOzvlJDDGlePToEbZu3Yrw8HD89ddfqFmzJvr27YvQ0FCtrtqrS0OHDsXGjRsxa9YsjcWsRo0ahQ8++ADz5s3TdYlUwZ06dQpjx47F1q1b0b17d0yePFkru6sXd+/ePZw6dQpVqlQpc0HCV+Xy5cto0KABjI2N8fnnn0tbgpw4cQILFy5Efn4+EhMT4erqKmsdgO4XBgWAOnXqwMPDAxMnTix1f7eKPvFWbi+yAams85MElen+/ftizpw5wtjYWKhUKmFkZCRCQ0PF1atXdV2a7PLz88XQoUOFkZGR0NPTEyqVShgbG4thw4aJ+/fv67o8qsCuXLkiPv30U2FoaCjatWsnjh07puuStObChQsiJCRE+ptUqVRCT09PBAcHi7Nnz2qtDisrK3HgwIESx/fv3y/UarVWajAzM9Pqa6byiSMxpUhMTMSyZcsQGRkJc3Nz9OzZE3379sXVq1fx3Xff4c6dOzhw4ICuy9SKe/fuaezeXBH3KKHyIScnB1OmTMGCBQtQp04dTJ8+Hc2bN5e93/K4xHx2djbOnj0L4PFcPm1ftabLhUGLtGjRAqNHj0ZISIjsfdHzy8vLw8OHDzWOyblNDUNMMXPmzMHy5ctx6tQptG3bFp9++ineffddaQl+ADh37hzefPNN2ScQ6kqfPn2eq92yZctkroTo/8yYMQPTp0+Hk5MTpkyZIusy5k/iEvMl6Wph0H///Vf67/Pnz+Pbb7/FqFGjSt3fTe7Vk+n/5Obm4quvvsKvv/6KGzdulLifeyfJ7Ny5c3B3d5fmvvTq1QtOTk6ltn3w4AHWrl372q5BoKenh6pVq6Ju3bpP3SVVztWLiZ6kp6cHU1NTtGrVCvr6+mW2e50vuy9PLl26hA4dOiA5ObnEwqCbN28u8wqq/6roKrWy3puK7uPEXu36/PPPsXv3bkyaNAmffPIJfvzxR1y5cgWLFy/GtGnT0L17d9n6ZojB4z+MypUrIyAgAC1atECLFi1QtWpVXZelE4MGDUJkZCSqVKmCPn36cIE1Hfvmm28QEBCAZs2aVehTeb169XquKyGWL18uax07d+5Ey5YtS70vLCwMgwcPlrX/8kbbC4NevHjxudtW1PdwXahSpQpWrFiBgIAAWFlZ4fDhw3B3d8fKlSuxdu1abNu2Tb7OdTITp5zZs2ePmDx5smjZsqUwMzMTenp6ws3NTfTp00esXLlSXL58WdclatX9+/fFmjVrRKtWrYSZmZn46KOPRFRUlCgsLNR1aRVOcHCwsLS0FEZGRqJJkyZizJgx4q+//hJ37tzRdWkVklqtLnVC69y5c4WlpaUOKtKunTt3ilq1aomcnJwS9926dUt4eXmJPXv26KAy0iVzc3ORmpoqhBCicuXKYv/+/UKIxxPRzc3NZe2bIeYJDx48ELGxsWLixIkiMDBQmJqaCj09PeHh4aHr0nQiNTVVTJgwQVSvXl24urryw1MHHj16JOLj48XUqVNFcHCwsLKyEoaGhqJx48a6Lq3CWbZsmbC3txfHjx+Xjs2cOVNYWVlViA/v9957T8yZM6fM++fPny/ef/99rdQSHh4u/vjjD+nnUaNGCbVaLXx9faUPVNIOHx8fERMTI4QQonXr1mLEiBFCiMe/D5UrV5a1b4aYMty7d0/s2LFDjBgxQlhZWQk9PT1dl6QTFy9eFBMnThTVqlUTlStXZojRoVOnTolFixaJDz/8UBgYGAh7e3tdl1QhzZw5U1SuXFmkpKSIadOmCSsrK7F3715dl6UVVapUESdOnCjz/pMnTwpXV1et1OLh4SF27twphBAiPj5emJqaisWLF4v33ntPdOzYUSs10GNz5swR8+fPF0IIsWvXLmFqaiqMjIyESqUS8+bNk7Vvzon5/+7fv4/4+Hjs3r0bMTExOHjwIKpVqwZ/f3/4+fnB398flStX1nWZWpGfn48NGzZg2bJliIuLQ7t27dC7d2+EhIRoXKlF8vvpp58QGxuL2NhYFBQUoHnz5vD390dAQACvvtChsWPHYsmSJSgoKEBUVFSFWdXbxMQEycnJcHd3L/X+c+fOwcfHR/aVkwHAzMxMWmjwq6++Qnp6OlasWIHjx48jICAAWVlZstdApUtLS0NiYiLc3d1lf5/i3kkA/P39cfDgQdSoUQN+fn4YMmQI/P39S2x3XxEUn9jbu3dvREZGan0/Fvo/n3/+OSpVqoQRI0ZgwIABsq63QKX74YcfShxzdnaGmZkZ/Pz8sH//fuzfvx/A45WuX2eVK1fGsWPHygwx//77r+x7SBWxsLDAjRs3UKVKFezYsQNffvklgMdBSxshioBdu3Zh8ODBSEhI0HhvqlKlCtRqNZo2bYpFixbJup4TR2IAGBoawtnZGe+//z4CAgLg5+cn+xLi5VXR7sl169Z96tUgvJRVOzZt2oQ9e/YgJiYGJ06cwNtvv42AgAAEBASgefPmsLCw0HWJr73n3dlepVLhwoULMlejW0OGDJFGqp/cIDcvLw+NGjVCYGBgqcHvVevevTtOnTqFunXrYu3atUhLS4OdnR22bNmCr7/+GsnJybLXUNG1b98egYGBUoB80g8//IDdu3fLuiQHQwweL9Tzzz//ICYmBrt370ZSUhI8PDykYXt/f/8Ks2dSebmUlUrKycnBP//8g99//x1r1qyBSqVCfn6+rsuqkLKysqCnp1fhRimvXbuGevXqQV9fH4MHD4anpydUKhVOnjyJH3/8EQUFBTh8+LBWRrFv3bqFb7/9FpcuXcLAgQOllXvHjx8PIyMjfPPNN7LXUNFVrVoVUVFR0v5ZTzp16hSCgoKQlpYmWw0MMaW4c+cO4uLipPkxR48eRc2aNZnsSSdu3ryJ2NhYxMTEICYmBsnJybCzs4O/vz9+++03XZdXYdy6dQvffPMN1q1bh+zsbACPd/Lt2rUr/ve//1WYDQcvXryIgQMHYvv27dKicyqVCsHBwVi4cCHc3Nx0WyBpTXmYI8U5MaUwNzeHra0tbG1tYWNjAwMDA5w8eVLXZVEF9NZbb+HEiROwtbWFn58f+vXrh4CAAHh7e+u6tArl5s2b8PX1lZbWr1WrFoQQOHnyJMLDw7Fz507Ex8fDxsZG16XKrmrVqti2bRuys7Nx7tw5CCFQs2ZNnb32e/fuIS0tDQ8ePNA4zonv8isPc6Q4EgOgsLAQiYmJ0umkvXv3Ijc3F5UrV0ZgYKB04wqQpG1hYWEMLeXAsGHDsHPnTvz9998lTpVkZGQgKCgILVu2xNy5c3VUYcWTlZWFXr16ISoqqtT7ue2A/MrDHCmGGDzeYTM3NxfOzs7SpMnAwEDUqFFD16URAXi8Z1dKSgpq1KgBAwMOoGqbm5sbFi9ejODg4FLvj4qKwoABA5Camqrdwiqw7t27IzU1FfPmzUNgYCA2btyIa9eu4fvvv8fs2bPRtm1bXZf42isXc6RkXYVGIRYtWiROnz6t6zKISrh3757o06eP0NfXF/r6+uL8+fNCCCGGDBkipk6dquPqKg4jIyNx6dKlMu+/dOmSMDY21mJF5OTkJC1vb2lpKb2Hb968WTRr1kyXpVUoqampok2bNkJPT0+oVCqhUqmEnp6eaNOmjUhJSZG9f65cBqB///7w8PDQdRlEJYwZMwZHjx5FTEyMxnBtq1atsG7dOh1WVrHY29s/dZQlJSWlwl2ppGu5ublwcHAAANja2kqL2/n4+ODw4cO6LK1CKZojdf36dezfvx8JCQm4fv06tm3bppVJ3gwxROXYpk2bEBYWhnfeeUfj0ncvLy+cP39eh5VVLCEhIfjmm29KTB4FHq9wPW7cOOkSX9IOT09PnD59GgBQp04dLF68GFeuXMGiRYu0tuAe/R8bGxs0bNgQjRo10uokb55cJyrHsrKypG+bxeXm5j7Xej70akycOBENGjRAzZo18fnnn+PNN98EAJw4cQILFy5Efn4+Vq5cqeMqK4Zz587B3d0dw4YNQ3p6OoDHa8MEBwdj9erVMDIyQnh4uG6LJK1hiCEqxxo2bIg///wTQ4YMAQApuCxZsgS+vr66LK1CeeONN7Bv3z4MGjQIY8eO1VgfpXXr1ggLC4Orq6uOq6wYPDw8NK4cTU1NRd26dZGamirtpVRRV1yviBhiiMqxqVOnIiQkBCdOnMCjR48wf/58HD9+HPv27UNsbKyuy6tQqlWrhr/++gvZ2dk4e/YsAMDd3R22trY6rqxiKdoQNSYmBoMHD8b9+/dRpUoVtGjRAoGBgRVyz7uKjJdYE5Vzx44dw6xZs3Do0CEUFhaiXr16+Oqrr+Dj46Pr0oh06uHDh9i3b5+0mnVCQgLy8/Ph7u4uzZeh1xtDDBERKVpeXh7i4uKwfft2LFmyBHfv3uVidxUEQwwRESnK/fv3ER8fL+1vd/DgQVSrVg3+/v7w8/ODv78/KleurOsySQsYYojKIT09vWdefaRSqfDo0SMtVURUPvj7++PgwYOoUaOGFFj8/f05F6aCYoghKoc2b95c5n3x8fFYsGABhBCy7g5LVB4ZGhrC2dkZ77//PgICAuDn58erkSowhhgihTh16hTGjh2LrVu3onv37pg8eTKqVKmi67KItCo3Nxf//POPtGFvUlISPDw84O/vj4CAAPj7+6NSpUq6LpO0hCGGqJy7evUqxo8fj4iICAQHB2Pq1Knc1Zro/7tz5w7i4uKk+TFHjx5FzZo1kZycrOvSSAu47QBROZWTk4OvvvoK7u7uOH78OHbu3ImtW7cywBAVY25uDltbW9ja2sLGxgYGBgY4efKkrssiLeFid0Tl0IwZMzB9+nQ4OTlh7dq16NChg65LIioXCgsLkZiYKJ1O2rt3L3Jzc6VVfH/88UcEBgbqukzSEp5OIiqH9PT0YGpqilatWkFfX7/Mdhs2bNBiVUS6Z2VlhdzcXDg7OyMgIAABAQEIDAxEjRo1dF0a6QBHYojKoU8++YQbPBKVYubMmQgMDISHh4euS6FygCMxREREpEic2EtERESKxBBDREREisQQQ0RERIrEEENERESKxBBDREREisQQQ0RERIrEEENEkszMTPTv3x9VqlSBsbExnJycpP2aVCrVU2/h4eEAgLy8PNjY2MDW1lbaZTs8PPyZj4+JiSmznYmJyXPV36tXL6hUKkybNk3j+KZNm8pcd8fT0xNGRka4cuVKifsCAgJKfT4AePfdd6FSqTBhwoQS7Z+8DRgw4LnqJ6IXwxBDRJIPPvgAR48eRUREBM6cOYMtW7YgICAAXl5eSE9Pl26dO3dGSEiIxrEuXboAANavXw9vb294eXlJKwp36dJFo62vry/69euncaxp06YAHq/IWvx4eno6Ll68+NyvwcTEBNOnT0d2dvYz28bFxeH+/fv46KOPpBD2JFdXVyxfvlzj2NWrV7Fr1y44OzuXaP/k60pPT8eMGTOeu34ien5csZeIAAC3bt1CXFwcYmJi4O/vDwCoWrUqGjVqVKKtqakp8vPz4eTkVOK+pUuXokePHhBCYOnSpejevTtMTU1hamoqtTEyMoKZmVmpj1epVKUef16tWrXCuXPnMHXq1GeGh6VLl6Jbt27w9/fH559/jq+//rrEiE27du3w66+/Yu/evWjWrBmAxyNLQUFBSEtLK/GcZb0uInr1OBJDRAAACwsLWFhYYNOmTcjPz3+p5zh//jz27duHzp07o3PnzoiPj8eFCxdecaVPp6+vjylTpmDBggW4fPlyme3u3LmD3377DT169EDr1q2Rm5uLmJiYEu2MjIzQvXt3jdGY8PBw9OnTR47yiegFMMQQEQDAwMAA4eHhiIiIgLW1NZo1a4avv/4a//7773M/x7Jly9CmTRtpTkxISAiWLVv2QnXk5ORIgaroFhQU9ELP0bFjR9SpUwfjx48vs01kZCRq1qyJ2rVrQ19fH127dsXSpUtLbdu3b1/8+uuvyM3NxZ49e5CTk4O2bduW2nbhwoUl6o+IiHih+ono+TDEEJHkgw8+wNWrV7FlyxYEBwcjJiYG9erVK3O+SHEFBQWIiIhAjx49pGM9evRAREQECgoKnrsGS0tLJCUladyenJPyPKZPn46IiAicOHGi1PuLTnsVr3XDhg24detWibZvvfUWatasid9//x3Lli1DaGgoDA0NS33e7t27l6i/Y8eOL1w/ET0b58QQkQYTExO0bt0arVu3xnfffYdPP/0U48ePR69evZ76uO3bt+PKlSvSBN8iBQUF2LFjB9q0afNc/evp6cHd3f1ly5f4+fkhODgYX3/9dYnaT5w4gf379+PgwYP46quvNGpdu3YtBg4cWOL5+vTpgx9//BEnTpzAgQMHyuxXrVa/kvqJ6Nk4EkNET+Xl5YXc3Nxntlu6dCm6du1aYhSie/fuZZ6mkdu0adOwdetWxMfHl6jVz88PR48e1ah19OjRZdbarVs3HDt2TLryioh0jyMxRAQAuHHjBj766CP06dMHb731FiwtLZGYmIgZM2agQ4cOT31sVlYWtm7dii1btsDb21vjvp49e6Jt27bIyspCpUqVnlmHEAIZGRkljjs4OEBP78W+d/n4+KB79+5YsGCBdOzhw4dYuXIlJk2aVKLWTz/9FDNmzMDRo0fx9ttva9xnY2OD9PT0Mk8jFbl3716J+o2NjWFjY/NCtRPRs3EkhogAPL46qXHjxpg7dy78/Pzg7e2NcePGoV+/fggLC3vqY1esWAFzc3O0bNmyxH2BgYGwtLTEypUrn6uO27dvw9nZucQtMzPzpV7X5MmTIYSQft6yZQtu3LhR6jyVmjVrwsfHp8zRGGtra5ibmz+1vyVLlpSo/eOPP36p2ono6VSi+F83ERERkUJwJIaIiIgUiSGGiBQhLS2txPorxW+lrZ5LRK83nk4iIkV49OgRUlNTy7zfzc0NBga8VoGoImGIISIiIkXi6SQiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUiSGGCIiIlIkhhgiIiJSJIYYIiIiUqT/B3XlK8CtK124AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "agg_gt25_df.plot('STATE_NAME','Point_Count', kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "California and Alaska clearly top the list with the most earthquakes. Let us view what the average intensity and minimum depth is in the plots below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<Axes: title={'center': 'min_Depth__kilometers_'}, xlabel='STATE_NAME'>,\n",
       "       <Axes: title={'center': 'mean_Event_Magnitude'}, xlabel='STATE_NAME'>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_gt25_df.plot('STATE_NAME',['min_Depth__kilometers_', 'mean_Event_Magnitude'],kind='bar', subplots=True)"
   ]
  }
 ],
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